docstr: Whether the logging outputs returned by `forward` can be summed
  across workers prior to calling `reduce_metrics`. Setting this
  to True will improves distributed training speed.
docstr_tok: ["Whether","the","logging","outputs","returned","by","`","forward","`","can","be","summed","across","workers","prior","to","calling","`","reduce_metrics","`",".","Setting","this","to","True","will","improves","distributed","training","speed","."]
code: def logging_outputs_can_be_summed() -> bool:
      """
      Whether the logging outputs returned by `forward` can be summed
      across workers prior to calling `reduce_metrics`. Setting this
      to True will improves distributed training speed.
      """
      return True
  
file_path: fairseq/examples/adaptive_span/adaptive_span_loss.py
imports: import math
  from dataclasses import dataclass
  import torch.nn.functional
  from fairseq import utils
  from fairseq.logging import metrics
  from fairseq.criterions import register_criterion
  from fairseq.criterions.cross_entropy import CrossEntropyCriterion
  from fairseq.dataclass import FairseqDataclass
  from omegaconf import II
label_desc: Use image node_id 5 for calling the AdaptiveSpanCriterion obj's underlying member method code with example usage: obj.logging_outputs_can_be_summed() and returns: True